An adaptive parameter binary-real coded genetic algorithm for constraint optimization problems: Performance analysis and estimation of optimal control parameters
An adaptive parameter binary-real coded genetic algorithm for constraint optimization problems: Performance analysis and estimation of optimal control parameters
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DOI：
10.1016/j.ins.2013.01.005
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发表时间：
2013-06-01
影响因子：
8.1
通讯作者：
Akama, Kiyoshi
中科院分区：
文献类型：
--
作者：
Abdul-Rahman, Omar Arif;Munetomo, Masaharu;Akama, Kiyoshi
实参数约束问题是现实世界中经常遇到的一类重要的优化问题。一方面，遗传算法是一种高效的搜索元启发式算法，是进化算法家族中的重要成员，已成功地应用于全局优化问题。然而，标准形式的遗传算子对约束的存在视而不见。因此，通过结合适当的处理技术将遗传算法扩展到约束优化问题是遗传算法研究的一个活跃方向。最近，我们提出了一种二进制实数编码遗传算法。BRGA是将合作二进制编码遗传算法(BGA)和实数编码遗传算法(RGA)相结合的一种新的混合遗传算法。它采用了一种基于参数的自适应混合方案，该方案以顺序时间交织的方式分配计算能力并调节协作版本之间的交互。在这项研究中，我们旨在通过在BRGA的体系结构中引入一种改进的动态惩罚函数来将BRGA扩展到约束问题。我们使用CEC 2010基准测试套件中的18个函数来分析BRGA的质量、时间和可伸缩性性能。为了考察改进算法的有效性，我们比较了BRGA在原始惩罚函数和改进惩罚函数下的性能。此外，为了证明BRGA的性能，我们将其与文献中的一些其他进化算法的性能进行了比较。我们还实现了一个稳健的参数调整过程，该过程依赖于统计测试、实验设计和响应面方法(RSM)的技术来估计控制参数的最佳值，以确保BRGA在处理特定问题时具有良好的性能。(C)2013 Elsevier Inc.保留所有权利。
Real parameter constrained problems are an important class of optimization problems that are encountered frequently in a variety of real world problems. On one hand, Genetic Algorithms (GAs) are an efficient search metaheuristic and a prominent member within the family of Evolutionary Algorithms (EAs), which have been applied successfully to global optimization problems. However, genetic operators in their standard forms are blind to the presence of constraints. Thus, the extension of GAs to constrained optimization problems by incorporating suitable handing techniques is an active direction within GAs research. Recently, we have proposed a Binary Real coded Genetic Algorithm (BRGA). BRGA is a new hybrid approach that combines cooperative Binary coded GA (BGA) with Real coded GA (RGA). It employs an adaptive parameter-based hybrid scheme that distributes the computational power and regulates the interactions between the cooperative versions, which operate in a sequential time-interleaving manner. In this study, we aim to extend BRGA to constrained problems by introducing a modified dynamic penalty function into the architecture of BRGA. We use the CEC'2010 benchmark suite of 18 functions to analyze the quality, time and scalability performance of BRGA. To investigate the effectiveness of the proposed modification, we compare the performance of BRGA under both the original and the modified penalty functions. Moreover, to demonstrate the performance of BRGA, we compare it with the performance of some other EAs from the literature. We also implement a robust parameter tuning procedure that relies on techniques from statistical testing, experimental design and Response Surface Methodology (RSM) to estimate the optimal values for the control parameters to secure a good performance by BRGA against specific problems at hand. (c) 2013 Elsevier Inc. All rights reserved.